Dask
Dask is an open-source Python library for parallel computing that scales Python workflows from laptops to clusters. It provides familiar interfaces like NumPy arrays, Pandas DataFrames, and scikit-learn, but can operate on datasets larger than memory by breaking them into smaller chunks and processing them in parallel.
Core Information
#core_informationName, description, vendor website, logo, technology type and tags.
Overview & Role
Dask is an open-source Python library for parallel computing that scales Python workflows from laptops to clusters. It provides familiar interfaces like NumPy arrays, Pandas DataFrames, and scikit-learn, but can operate on datasets larger than memory by breaking them into smaller chunks and processing them in parallel.
Domain Classification & Tags
Performance Profile
#performance_profileTypical read and write throughput, payload size and processing latency.
Read Performance Profile
Throughput: Varies greatly with workload and cluster size (e.g., 100s MB/s to TB/s for large dataframes)
Payload Size: Varies greatly with data type and task (e.g., KB for small arrays, GB for large dataframes)
Processing Latency: Varies greatly with task complexity and cluster size (e.g., 10s ms for simple reads, seconds for complex aggregations)
Write Performance Profile
Throughput: Varies greatly with workload and cluster size (e.g., 100s MB/s to TB/s for large dataframes)
Payload Size: Varies greatly with data type and task (e.g., KB for small arrays, GB for large dataframes)
Processing Latency: Varies greatly with task complexity and cluster size (e.g., 10s ms for simple writes, seconds for complex transformations)
Architecture Diagram
#architecture_diagramReference diagram of the technology's internal architecture.
Features
#featuresCatalogued product capabilities and what each one does.
Empirical data for features is currently being compiled in the global catalog.
Typical Use Cases
#typical_use_casesScenarios the technology is commonly chosen for.
Empirical data for typical use cases is currently being compiled in the global catalog.
Known Customers
#known_customersPublicly referenced organisations using the technology.
Empirical data for known customers is currently being compiled in the global catalog.
Known Integrations
#known_integrationsOther products and services it is documented to work with.
Empirical data for known integrations is currently being compiled in the global catalog.
Connectors
#connectorsDirectional data connections to other technologies, with direction and maturity.
Empirical data for connectors is currently being compiled in the global catalog.
Reference Architectures
#reference_architecturesPublished architectures where the technology is used or mentioned.
No published reference architecture blueprints currently link to Dask.
Security Features
#security_featuresBuilt-in security and access-control capabilities.
Empirical data for security features is currently being compiled in the global catalog.
Known Issues
#known_issuesDocumented limitations, defects and operational pitfalls.
Empirical data for known issues is currently being compiled in the global catalog.
Guidelines
#guidelinesRecommended practices for adopting and operating the technology.
Empirical data for guidelines is currently being compiled in the global catalog.
Standards & Compliance
#standards_and_complianceStandards, certifications and control requirements it maps to.
Empirical data for standards & compliance is currently being compiled in the global catalog.
Sources
#sourcesDocumentation and research references behind the recorded information.
Empirical data for sources is currently being compiled in the global catalog.
Expert Validation
#expert_validationWhether domain experts reviewed and confirmed the content.
Empirical data for expert validation is currently being compiled in the global catalog.